CN108920716B - Data retrieval and visualization system and method based on knowledge graph - Google Patents
Data retrieval and visualization system and method based on knowledge graph Download PDFInfo
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Abstract
The invention discloses a data retrieval and visualization system based on a knowledge graph, which comprises: a presentation layer: providing a search entry and a display interface, providing a service layer to interact with a data layer, and visually displaying data; and (3) a service layer: exchanging data with the data layer, and analyzing the service access submitted by the presentation layer; and (3) a data layer: and establishing a relation between the ontology models, indicating the ontology and the association parameters participating in association, acquiring all entity data corresponding to the ontology, establishing an association relation, and storing all the ontology data, the entity data and the relationship data into a graph database. The method and the system can visually and efficiently show the retrieval result to the user, support the visualization of the combat data in different scenes, ensure the safe and controllable transmission of the sensitive information and data, and meet the personalized and intelligent requirements of the retrieval result.
Description
Technical Field
The invention relates to data management of an information system, in particular to a data retrieval and visualization system and method based on a knowledge graph.
Background
At present, along with the development of an information system of our army, combat data presents the characteristics of large scale, wide distribution, dynamic structural change, complex and various modes and the like, and the data have great application value, but at present, the data utilization rate is not much, the data are only piled up together, but participate in business decision, and the data for business analysis and business control are very little; meanwhile, business data are dispersed in different systems, query data need to enter different systems, the efficiency is low by using the traditional data retrieval query technology, the relational computation amount among the data is large and complex, and the application difficulty of the business data is very large; the traditional retrieved data result is more detailed and simple in display, only basic data summarizing and displaying functions can be realized, the rule of analysis data and the relation between the rule and the analysis data cannot be visually and abundantly displayed, and early warning and prediction of services cannot be performed through a model. How to visually and efficiently show the retrieval result to the user, support the visualization of combat data in different scenes, ensure the safe and controllable transmission of sensitive information and data, and meet the individual and intelligent requirements of the retrieval result.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a data retrieval and visualization system and method based on a knowledge graph, which can visually and efficiently show a retrieval result to a user, support the visualization of combat data in different scenes, ensure the safe and controllable transmission of sensitive information and data and meet the personalized and intelligent requirements of the retrieval result.
The technical scheme is as follows: the invention discloses a data retrieval and visualization system based on a knowledge graph, which comprises:
a presentation layer: providing a search entry and a display interface, providing a service layer to interact with a data layer, and visually displaying data;
and (3) a service layer: exchanging data with the data layer, and analyzing the service access submitted by the presentation layer;
and (3) a data layer: and establishing a relation between the ontology models, indicating the ontology and the association parameters participating in association, acquiring all entity data corresponding to the ontology, establishing an association relation, and storing all the ontology data, the entity data and the relationship data into a graph database.
The method for carrying out retrieval management configuration by adopting the data retrieval and visualization system based on the knowledge graph comprises the following steps:
s11: acquiring all constructed body models, acquiring names of all body models and carrying out corresponding remarks;
s12: constructing an ontology attribute configuration tool, and configuring whether the attribute in the ontology model needs to support the index;
s13: an ontology configuration tool is built, index category setting of an ontology model is supported, the retrieval categories to which the ontology model is retrieved are divided, and an accurate positioning search range is provided for search terms; performing alias setting on the ontology model; setting fields of the ontology model; and when the set ontology model is the media model, setting the media model, wherein the setting comprises selecting corresponding attributes from the media attributes as a media name, a media format and media content.
The method for performing associated query by adopting the data retrieval and visualization system based on the knowledge graph comprises the following steps:
s21: inputting a query keyword;
s22: querying the ontology in the relationship between the constructed ontology models;
s23: querying data of the associated entity by using an entity relation constructed by the associated parameters of the ontology model;
s24: and analyzing and fusing the entity relationship and the acquired data of the associated entity, and returning the final query result to the user.
The method for visually displaying the data retrieval and visualization system based on the knowledge graph comprises the following steps:
s31: inputting query words and query conditions, and sending query contents to a graph database by the system to traverse all relevant nodes;
s32: selecting a proper display mode according to the obtained index category to which the node belongs;
s33: and displaying the acquired data.
The method for automatically recording and recommending the search terms by adopting the data search and visualization system based on the knowledge graph is characterized by comprising the following steps of: the method comprises the following steps: inputting search terms, and screening and matching the search terms with the search term history records stored in the local memory by the system: if the input search word exists in the history record, deleting the original search word in the history record, and storing the search word of the current time into the latest history record; if the input search word is not in the history record, the search word of the current time is stored in the latest history record.
The method for Chinese word segmentation by adopting the data retrieval and visualization system based on the knowledge graph is characterized by comprising the following steps: the method comprises the following steps: constructing a domain ontology, taking the constructed domain ontology semantic dictionary as a word segmentation dictionary, and then respectively carrying out forward maximum matching on the text to be segmented based on the semantic dictionary: and matching the constructed semantic dictionary, and outputting a corresponding word segmentation result if the matching in the semantic dictionary is successful.
Has the advantages that: the invention discloses a data retrieval and visualization system and a data retrieval and visualization method based on a knowledge graph, which can visually and efficiently show a retrieval result to a user, support the visualization of combat data in different scenes, ensure the safe and controllable transmission of sensitive information and data, and meet the personalized and intelligent requirements of the retrieval result.
Drawings
FIG. 1 is a diagram of a software hierarchy of a system in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart of an entity association query based on a knowledge-graph in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of data visualization according to an embodiment of the present invention;
FIG. 4 is a diagram of a module for automatically recording and recommending search terms in accordance with an embodiment of the present invention;
FIG. 5 is a flowchart of a Chinese word segmentation method based on semantic information and dictionary combination according to an embodiment of the present invention.
Detailed Description
The specific embodiment discloses a data retrieval and visualization system based on a knowledge graph, as shown in fig. 1, comprising:
a performance layer: providing a search entry and a display interface, providing a service layer to interact with a data layer, and visually displaying data;
and (3) a service layer: adopting Web Service to exchange data with the data layer, and analyzing the Service access submitted by the presentation layer; the index module is mainly used for analyzing the data acquired by the acquisition module and establishing a distributed index for the data;
and (3) a data layer: the system comprises a data storage module and a data analysis module; the method is used for constructing the relationship between the ontology models, indicating the ontology and the association parameters participating in association, acquiring all entity data corresponding to the ontology, constructing the association relationship, and storing all the ontology data, the entity data and the relationship data into a graph database.
The method for carrying out retrieval management configuration by adopting the system comprises the following steps:
s11: acquiring all constructed body models, acquiring names of all body models and carrying out corresponding remarks;
s12: constructing an ontology attribute configuration tool, and configuring whether the attributes in the ontology model need to support indexing or not, because most models only need to index part of the attributes; setting label display of attributes as required, displaying corresponding attribute results in a final search page, and simultaneously setting a label display sequence to arrange the attributes most concerned by a user in the front of the result page; aiming at the problems that part of attribute names in the table are too long, english is used for short and the like, an alias editing function can be used, the names are changed into names which are easier to understand, and the names are displayed on a final search page;
s13: constructing a body configuration tool, supporting index type setting of the body model, dividing the retrieval type to which the body model retrieval belongs, and providing an accurate positioning search range for the search terms; performing alias setting on the ontology model, and editing obscure and unintelligible letters into easily understood words for short; setting fields of the ontology model, wherein the field setting can select a name field and a custom name field; and when the set ontology model is the media model, setting the media model, wherein the setting comprises selecting corresponding attributes from the media attributes as a media name, a media format and media content.
The method for performing association query by using the system, as shown in fig. 2, includes the following steps:
s21: inputting a query keyword; for example, the input "F16 fighter";
s22: inquiring the ontology in the relationship between the constructed ontology models; for example, querying an "equipment" ontology in an already constructed knowledge graph, and querying an entity of which the "equipment name" is "F16 fighter" in the ontology;
s23: querying data of the associated entity by using an entity relation constructed by the associated parameters of the ontology model; for example, entities such as "army base", "equipment media", etc. associated with the "F16 fighter" entity;
s24: analyzing and fusing the entity relationship and the obtained data of the associated entity, and returning the final query result to the user; for example, equipment performance information of the 'F16 fighter' is returned, and associated deployment position, equipment picture and other data are returned.
The method for performing visual display by using the system, as shown in fig. 3, includes the following steps:
s31: inputting query words and query conditions, and sending query contents to a graph database by the system to traverse all relevant nodes;
s32: selecting a proper display mode according to the obtained index category to which the node belongs;
s33: and displaying the acquired data, taking searching equipment of the current party as an example, displaying the equipment attribute in a key value pair mode, displaying the equipment performance in a table mode, displaying equipment photos in a picture mode, and displaying the relation among the equipment in a knowledge map mode.
The method for automatically recording and recommending the search terms by adopting the system comprises the following processes as shown in fig. 4: inputting search terms, and screening and matching the search terms with the search term history records stored in the local memory by the system: if the input search word exists in the history record, deleting the original search word in the history record, and storing the search word of the current time into the latest history record; if the input search word is not in the history record, the search word of the current time is stored in the recent history record.
The method for Chinese word segmentation by adopting the system comprises the following processes: constructing a domain ontology, taking the constructed domain ontology semantic dictionary as a word segmentation dictionary, and then respectively carrying out forward maximum matching on the text to be segmented based on the semantic dictionary: and matching the constructed semantic dictionary, and outputting a corresponding word segmentation result if the matching in the semantic dictionary is successful. For example: in the section of "the american air force F16 fighter", the semantic dictionary includes words such as "the american air force", "the united states", "F16", "fighter", and the like. The words are cut out when the words are scanned backwards in sequence from the American word, the American word and the American air force are respectively taken for matching, and the longest matching character string in the dictionary is the American air force. The scan is then started with the "yes" word and the above operation is repeated.
The specific flow chart of the chinese word segmentation method is shown in fig. 5, assuming that a chinese word segmentation algorithm based on semantic information and dictionary combination is performed on D = D1D2D3D4 \8230dn, the algorithm process is described as follows:
(1) And taking out the first character D1 in the D, comparing the first character D1 with the loaded semantic dictionary, and marking the first character D1 when a word with the D1 as a prefix exists.
(2) And D2 is taken out from D and compared with the dictionary to be matched, and whether the word with D1D2 as the prefix exists is judged.
(3) If not, the D1 word string is segmented from D, and the segmentation is finished once.
(4) If yes, judging to calculate the number n of the words with D1D2 as the prefix.
(5) If n =0, the sub-word ends.
(6) If n is not 0, then taking out Di from D, matching with the dictionary to judge whether a word with Di \8230andDn-lDn as prefix exists.
(7) If so, go to (6).
(8) If not, then the D1.
(9) And continuing to perform word segmentation from the character Di of the character string D, and repeating the steps until the forward segmentation of the character string D is finished.
Claims (5)
1. The associated query method of the data retrieval and visualization system based on the knowledge graph is characterized in that: the data retrieval and visualization system comprises:
a presentation layer: providing a search entry and a display interface, providing a service layer to interact with a data layer, and visually displaying data;
and (3) a service layer: exchanging data with the data layer, and analyzing the service access submitted by the presentation layer;
and (3) a data layer: the system comprises a database, a body model and a relationship database, wherein the database is used for establishing a relationship between body models, indicating bodies and association parameters participating in association, acquiring all entity data corresponding to the bodies, establishing an association relationship, and storing all the body data, the entity data and the relationship data into the database;
the correlation query method comprises the following steps:
s21: inputting a query keyword;
s22: inquiring the ontology in the relationship between the constructed ontology models;
s23: querying data of the associated entity by using an entity relation constructed by the associated parameters of the ontology model;
s24: and analyzing and fusing the entity relationship and the obtained data of the associated entity, and returning the final query result to the user.
2. The associative query method for data retrieval and visualization system based on knowledge graph according to claim 1, wherein the data retrieval and visualization system is further used for a visualization presentation method, and the visualization presentation method specifically comprises the following steps:
s31: inputting query words and query conditions, and sending query contents to a graph database by a system to traverse all related nodes;
s32: selecting a proper display mode according to the obtained index category to which the node belongs;
s33: and displaying the acquired data.
3. The method of claim 1, wherein the method comprises: the data retrieval and visualization system is also used for a retrieval management configuration method, and the retrieval management configuration method specifically comprises the following steps:
s11: acquiring all constructed body models, acquiring names of all body models and carrying out corresponding remarks;
s12: constructing an ontology attribute configuration tool, and configuring whether the attribute in the ontology model needs to support the index;
s13: an ontology configuration tool is built, index category setting of an ontology model is supported, the retrieval categories to which the ontology model is retrieved are divided, and an accurate positioning search range is provided for search terms; performing alias setting on the ontology model; setting fields of the ontology model; and when the set ontology model is the media model, setting the media model, wherein the setting comprises selecting corresponding attributes from the media attributes as a media name, a media format and media content.
4. The method of claim 1, wherein the query is associated with a knowledge-graph based data retrieval and visualization system, and wherein the query comprises: the data retrieval and visualization system is also used for a retrieval word automatic recording and recommendation method, and the retrieval word automatic recording and recommendation method specifically comprises the following steps:
inputting search words, and screening and matching the search words with the search word history records stored in the local memory by the system; if the input search word exists in the history record, deleting the original search word in the history record, and storing the search word of the current time into the latest history record; if the input search word is not in the history record, the search word of the current time is stored in the latest history record.
5. The method of claim 1, wherein the query is associated with a knowledge-graph based data retrieval and visualization system, and wherein the query comprises: the data retrieval and visualization system is also used for a Chinese word segmentation method, and the Chinese word segmentation method specifically comprises the following steps:
constructing a domain ontology, taking the constructed domain ontology semantic dictionary as a word segmentation dictionary, and then respectively performing forward maximum matching on the text to be segmented based on the semantic dictionary; and matching the constructed semantic dictionary, and outputting a corresponding word segmentation result if the matching in the semantic dictionary is successful.
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